Tootfinder

VLM-UDMC: VLM-Enhanced Unified Decision-Making and Motion Control for Urban Autonomous Driving
Haichao Liu, Haoren Guo, Pei Liu, Benshan Ma, Yuxiang Zhang, Jun Ma, Tong Heng Lee
https://arxiv.org/abs/2507.15266

VLM-UDMC: VLM-Enhanced Unified Decision-Making and Motion Control for Urban Autonomous Driving
Scene understanding and risk-aware attentions are crucial for human drivers to make safe and effective driving decisions. To imitate this cognitive ability in urban autonomous driving while ensuring the transparency and interpretability, we propose a vision-language model (VLM)-enhanced unified decision-making and motion control framework, named VLM-UDMC. This framework incorporates scene reasoning and risk-aware insights into an upper-level slow system, which dynamically reconfigures the optim…

Explainable AI for Maritime Autonomous Surface Ships (MASS): Adaptive Interfaces and Trustworthy Human-AI Collaboration
Zhuoyue Zhang, Haitong Xu
https://arxiv.org/abs/2509.15959

Explainable AI for Maritime Autonomous Surface Ships (MASS): Adaptive Interfaces and Trustworthy Human-AI Collaboration
Autonomous navigation in maritime domains is accelerating alongside advances in artificial intelligence, sensing, and connectivity. Opaque decision-making and poorly calibrated human-automation interaction remain key barriers to safe adoption. This article synthesizes 100 studies on automation transparency for Maritime Autonomous Surface Ships (MASS) spanning situation awareness (SA), human factors, interface design, and regulation. We (i) map the Guidance-Navigation-Control stack to shore-base…

Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control
Max Studt, Georg Schildbach
https://arxiv.org/abs/2509.15799 https://arxiv.o…

Hierarchical Reinforcement Learning with Low-Level MPC for Multi-Agent Control
Achieving safe and coordinated behavior in dynamic, constraint-rich environments remains a major challenge for learning-based control. Pure end-to-end learning often suffers from poor sample efficiency and limited reliability, while model-based methods depend on predefined references and struggle to generalize. We propose a hierarchical framework that combines tactical decision-making via reinforcement learning (RL) with low-level execution through Model Predictive Control (MPC). For the case o…

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning
Pan Hu
https://arxiv.org/abs/2507.14903

CoMoCAVs: Cohesive Decision-Guided Motion Planning for Connected and Autonomous Vehicles with Multi-Policy Reinforcement Learning
Autonomous driving demands reliable and efficient solutions to closely related problems such as decision-making and motion planning. In this work, decision-making refers specifically to highway lane selection, while motion planning involves generating control commands (such as speed and steering) to reach the chosen lane. In the context of Connected Autonomous Vehicles (CAVs), achieving both flexible and safe lane selection alongside precise trajectory execution remains a significant challenge.…

Towards Emergency Scenarios: An Integrated Decision-making Framework of Multi-lane Platoon Reorganization
Aijing Kong, Chengkai Xu, Xian Wu, Xinbo Chen, Peng Hang
https://arxiv.org/abs/2506.16311

Towards Emergency Scenarios: An Integrated Decision-making Framework of Multi-lane Platoon Reorganization
To enhance the ability for vehicle platoons to respond to emergency scenarios, a platoon distribution reorganization decision-making framework is proposed. This framework contains platoon distribution layer, vehicle cooperative decision-making layer and vehicle planning and control layer. Firstly, a reinforcement-learning-based platoon distribution model is presented, where a risk potential field is established to quantitatively assess driving risks, and a reward function tailored to the platoo…

Asset control and power in America is centered among old people with lots of assets.
And they associate protecting the stock market with protecting America.
The “number go up” rule is not a story of greed or economics,
it’s a story of how we make decisions as a society.
Take the pandemic.
Some of you might remember that right before Covid hit in force, I was freaking out. I pay attention to China, and it wasn’t hard to imagine that a contagious disease would…

The Number Go Up Rule: Why America Refuses to Fix Anything
In the Booming Twenties, all decision-making is about protecting the value of financial assets held by older people. Therefore, the number must go up. And nothing else matters.

Categorical Policies: Multimodal Policy Learning and Exploration in Continuous Control
SM Mazharul Islam, Manfred Huber
https://arxiv.org/abs/2508.13922 https://

Categorical Policies: Multimodal Policy Learning and Exploration in Continuous Control
A policy in deep reinforcement learning (RL), either deterministic or stochastic, is commonly parameterized as a Gaussian distribution alone, limiting the learned behavior to be unimodal. However, the nature of many practical decision-making problems favors a multimodal policy that facilitates robust exploration of the environment and thus to address learning challenges arising from sparse rewards, complex dynamics, or the need for strategic adaptation to varying contexts. This issue is exacerb…

Interviews with Shari Redstone and others detail her decision to sell her family's empire, Paramount's settlement with President Trump, her reputation, and more (James B. Stewart/New York Times)
https://www.nytimes.com/2025/08/19/busines

Why Did Shari Redstone Give Up Control of Paramount?
The media mogul spoke at length for the first time about selling her family’s empire, Paramount’s settlement with President Trump — and getting her reputation battered along the way.

Data-Driven Abstraction and Synthesis for Stochastic Systems with Unknown Dynamics
Mahdi Nazeri, Thom Badings, Anne-Kathrin Schmuck, Sadegh Soudjani, Alessandro Abate
https://arxiv.org/abs/2508.15543

Data-Driven Abstraction and Synthesis for Stochastic Systems with Unknown Dynamics
We study the automated abstraction-based synthesis of correct-by-construction control policies for stochastic dynamical systems with unknown dynamics. Our approach is to learn an abstraction from sampled data, which is represented in the form of a finite Markov decision process (MDP). In this paper, we present a data-driven technique for constructing finite-state interval MDP (IMDP) abstractions of stochastic systems with unknown nonlinear dynamics. As a distinguishing and novel feature, our te…

Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning
Anis Koubaa, Khaled Gabr
https://arxiv.org/abs/2509.13352 https://ar…

Agentic UAVs: LLM-Driven Autonomy with Integrated Tool-Calling and Cognitive Reasoning
Unmanned Aerial Vehicles (UAVs) are increasingly deployed in defense, surveillance, and disaster response, yet most systems remain confined to SAE Level 2--3 autonomy. Their reliance on rule-based control and narrow AI restricts adaptability in dynamic, uncertain missions. Existing UAV frameworks lack context-aware reasoning, autonomous decision-making, and ecosystem-level integration; critically, none leverage Large Language Model (LLM) agents with tool-calling for real-time knowledge access. …

Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks
Yu Zhang, Bokui Wan, Yongli Qin
https://arxiv.org/abs/2509.13944 …

Bridging Control Variates and Regression Adjustment in A/B Testing: From Design-Based to Model-Based Frameworks
A B testing serves as the gold standard for large scale, data driven decision making in online businesses. To mitigate metric variability and enhance testing sensitivity, control variates and regression adjustment have emerged as prominent variance reduction techniques, leveraging pre experiment data to improve estimator performance. Over the past decade, these methods have spawned numerous derivatives, yet their theoretical connections and comparative properties remain underexplored. In this p…

Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Shengbo Wang, Nian Si
https://arxiv.org/abs/2509.14203 https://a…

Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
Learning and optimal control under robust Markov decision processes (MDPs) have received increasing attention, yet most existing theory, algorithms, and applications focus on finite-horizon or discounted models. The average-reward formulation, while natural in many operations research and management contexts, remains underexplored. This is primarily because the dynamic programming foundations are technically challenging and only partially understood, with several fundamental questions remaining…

Nexstar and Sinclair control 63 of ABC's 205 affiliate stations; Nexstar's proposed $6B merger with Tegna, which requires FCC approval, could give it 13 more (New York Times)
https://www.nytimes.com/interactive/2025/09/19/business/media/abc-nexst…

How Nexstar’s Proposed TV Merger Is Tied to Jimmy Kimmel’s Suspension
Behind ABC’s decision was a display of how influential members of the Trump administration have become in shaping how media companies operate.

Structural Abstraction and Refinement for Probabilistic Programs
Guanyan Li, Juanen Li, Zhilei Han, Peixin Wang, Hongfei Fu, Fei He
https://arxiv.org/abs/2508.12344 https://

Structural Abstraction and Refinement for Probabilistic Programs
In this paper, we present structural abstraction refinement, a novel framework for verifying the threshold problem of probabilistic programs. Our approach represents the structure of a Probabilistic Control-Flow Automaton (PCFA) as a Markov Decision Process (MDP) by abstracting away statement semantics. The maximum reachability of the MDP naturally provides a proper upper bound of the violation probability, termed the structural upper bound. This introduces a fresh ``structural'' characterizati…

European policymakers are edging towards a decision that could force technology firms to scan encrypted messages in a bid to tackle child abuse material, reviving a debate that cuts to the heart of online privacy and security.
https://www.computing.co.uk/news/2…

EU moves closer to controversial ‘Chat Control’ law
European policymakers are edging towards a decision that could force technology firms to scan encrypted messages in a bid to tackle child abuse material, reviving ...

Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics
Rafael Zimmer, Oswaldo Luiz do Valle Costa
https://arxiv.org/abs/2509.12456

Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics
Reinforcement Learning has emerged as a promising framework for developing adaptive and data-driven strategies, enabling market makers to optimize decision-making policies based on interactions with the limit order book environment. This paper explores the integration of a reinforcement learning agent in a market-making context, where the underlying market dynamics have been explicitly modeled to capture observed stylized facts of real markets, including clustered order arrival times, non-stati…

Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration
Liangxuan Guo, Bin Zhu, Qingqian Tao, Kangning Liu, Xun Zhao, Xianzhe Qin, Jin Gao, Guangfu Hao
https://arxiv.org/abs/2509.11067

Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration
Autonomous agents for desktop automation struggle with complex multi-step tasks due to poor coordination and inadequate quality control. We introduce \textsc{Agentic Lybic}, a novel multi-agent system where the entire architecture operates as a finite-state machine (FSM). This core innovation enables dynamic orchestration. Our system comprises four components: a Controller, a Manager, three Workers (Technician for code-based operations, Operator for GUI interactions, and Analyst for decision su…

Omega-regular Verification and Control for Distributional Specifications in MDPs
S. Akshay (Dept of CSE, Indian Institute of Technology Bombay), Ouldouz Neysari (Singapore Management University, University of Tehran), {\DJ}or{\dj}e \v{Z}ikeli\'c (Singapore Management University)
https://arxiv.org/abs/2507.04286

Omega-regular Verification and Control for Distributional Specifications in MDPs
A classical approach to studying Markov decision processes (MDPs) is to view them as state transformers. However, MDPs can also be viewed as distribution transformers, where an MDP under a strategy generates a sequence of probability distributions over MDP states. This view arises in several applications, even as the probabilistic model checking problem becomes much harder compared to the classical state transformer counterpart. It is known that even distributional reachability and safety probl…

HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants
Benjamin Sturgeon, Daniel Samuelson, Jacob Haimes, Jacy Reese Anthis
https://arxiv.org/abs/2509.08494

HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants
As humans delegate more tasks and decisions to artificial intelligence (AI), we risk losing control of our individual and collective futures. Relatively simple algorithmic systems already steer human decision-making, such as social media feed algorithms that lead people to unintentionally and absent-mindedly scroll through engagement-optimized content. In this paper, we develop the idea of human agency by integrating philosophical and scientific theories of agency with AI-assisted evaluation me…

Unified Conformalized Multiple Testing with Full Data Efficiency
Yuyang Huo, Xiaoyang Wu, Changliang Zou, Haojie Ren
https://arxiv.org/abs/2508.12085 https://

Unified Conformalized Multiple Testing with Full Data Efficiency
Conformalized multiple testing offers a model-free way to control predictive uncertainty in decision-making. Existing methods typically use only part of the available data to build score functions tailored to specific settings. We propose a unified framework that puts data utilization at the center: it uses all available data-null, alternative, and unlabeled-to construct scores and calibrate p-values through a full permutation strategy. This unified use of all available data significantly impro…

Yes. We are.
The next epidemic *will affect you* before the mass media has received any mention of it from the Federal government.
It will be like AIDS except that the ignored dead will be everyone instead of just gays, drug injectors, and Haitians.
https://

Vaccine disputes and agency departures put U.S. at risk, a former CDC official says
Health and Human Services Deputy Secretary Jim O'Neill will be named interim director of the Centers for Disease Control and Prevention, according to a letter Kennedy sent to CDC staff, which was obtained by NPR. The decision comes after Health Secretary Robert F. Kennedy Jr. fired Susan Monarez, …

A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Contro
Sebastian Hirt, Lukas Theiner, Maik Pfefferkorn, Rolf Findeisen
https://arxiv.org/abs/2508.12738

A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Contro
Many control problems require repeated tuning and adaptation of controllers across distinct closed-loop tasks, where data efficiency and adaptability are critical. We propose a hierarchical Bayesian optimization (BO) framework that is tailored to efficient controller parameter learning in sequential decision-making and control scenarios for distinct tasks. Instead of treating the closed-loop cost as a black-box, our method exploits structural knowledge of the underlying problem, consisting of a…

Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation
Kanchon Gharami, Hansaka Aluvihare, Shafika Showkat Moni, Berker Pek\"oz
https://arxiv.org/abs/2509.00973

Clone What You Can't Steal: Black-Box LLM Replication via Logit Leakage and Distillation
Large Language Models (LLMs) are increasingly deployed in mission-critical systems, facilitating tasks such as satellite operations, command-and-control, military decision support, and cyber defense. Many of these systems are accessed through application programming interfaces (APIs). When such APIs lack robust access controls, they can expose full or top-k logits, creating a significant and often overlooked attack surface. Prior art has mainly focused on reconstructing the output projection la…

Enhancing Autonomous Manipulator Control with Human-in-loop for Uncertain Assembly Environments
Ashutosh Mishra, Shreya Santra, Hazal Gozbasi, Kentaro Uno, Kazuya Yoshida
https://arxiv.org/abs/2507.11006

Enhancing Autonomous Manipulator Control with Human-in-loop for Uncertain Assembly Environments
This study presents an advanced approach to enhance robotic manipulation in uncertain and challenging environments, with a focus on autonomous operations augmented by human-in-the-loop (HITL) control for lunar missions. By integrating human decision-making with autonomous robotic functions, the research improves task reliability and efficiency for space applications. The key task addressed is the autonomous deployment of flexible solar panels using an extendable ladder-like structure and a robo…

Modeling bias in decision-making attractor networks
Safaan Sadiq
https://arxiv.org/abs/2508.07471 https://arxiv.org/pdf/2508.07471

Modeling bias in decision-making attractor networks
Attractor neural network models of cortical decision-making circuits represent them as dynamical systems in the state space of neural firing rates with the attractors of the network encoding possible decisions. While the attractors of these models are well studied, far less attention is paid to the basins of attraction even though their sizes can be said to encode the biases towards the corresponding decisions. The parameters of an attractor network control both the attractors and the basins of…

Lightweight Auto-bidding based on Traffic Prediction in Live Advertising
Bo Yang, Ruixuan Luo, Junqi Jin, Han Zhu
https://arxiv.org/abs/2508.06069 https://…

Lightweight Auto-bidding based on Traffic Prediction in Live Advertising
Internet live streaming is widely used in online entertainment and e-commerce, where live advertising is an important marketing tool for anchors. An advertising campaign hopes to maximize the effect (such as conversions) under constraints (such as budget and cost-per-click). The mainstream control of campaigns is auto-bidding, where the performance depends on the decision of the bidding algorithm in each request. The most widely used auto-bidding algorithms include Proportional-Integral-Derivat…

Toward Optimal Statistical Inference in Noisy Linear Quadratic Reinforcement Learning over a Finite Horizon
Bo Pan, Jianya Lu, Yafei Wang, Hao Li, Bei Jiang, Linglong Kong
https://arxiv.org/abs/2508.08436

Toward Optimal Statistical Inference in Noisy Linear Quadratic Reinforcement Learning over a Finite Horizon
Recent developments in Reinforcement learning have significantly enhanced sequential decision-making in uncertain environments. Despite their strong performance guarantees, most existing work has focused primarily on improving the operational accuracy of learned control policies and the convergence rates of learning algorithms, with comparatively little attention to uncertainty quantification and statistical inference. Yet, these aspects are essential for assessing the reliability and variabili…

Curiosity-Driven Testing for Sequential Decision-Making Process
Junda He, Zhou Yang, Jieke Shi, Chengran Yang, Kisub Kim, Bowen Xu, Xin Zhou, David Lo
https://arxiv.org/abs/2509.02025

Curiosity-Driven Testing for Sequential Decision-Making Process
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent advances in Deep Learning (DL) have led to mature solutions for solving these complex problems, SDMs remain vulnerable to learning unsafe behaviors, posing significant risks in safety-critical applications. However, developing a testing framework for SDMs that can identify a diverse set of crash-triggering scenarios rema…

ControlMed: Adding Reasoning Control to Medical Language Model
Sung-Min Lee, Siyoon Lee, Juyeon Kim, Kyungmin Roh
https://arxiv.org/abs/2507.22545 https://…

ControlMed: Adding Reasoning Control to Medical Language Model
Reasoning Large Language Models (LLMs) with enhanced accuracy and explainability are increasingly being adopted in the medical domain, as the life-critical nature of clinical decision-making demands reliable support. Despite these advancements, existing reasoning LLMs often generate unnecessarily lengthy reasoning processes, leading to significant computational overhead and response latency. These limitations hinder their practical deployment in real-world clinical environments. To address thes…

Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control
Vishal Pandey, Debasmita Biswas
https://arxiv.org/abs/2508.21505

Spiking Decision Transformers: Local Plasticity, Phase-Coding, and Dendritic Routing for Low-Power Sequence Control
Reinforcement learning agents based on Transformer architectures have achieved impressive performance on sequential decision-making tasks, but their reliance on dense matrix operations makes them ill-suited for energy-constrained, edge-oriented platforms. Spiking neural networks promise ultra-low-power, event-driven inference, yet no prior work has seamlessly merged spiking dynamics with return-conditioned sequence modeling. We present the Spiking Decision Transformer (SNN-DT), which embeds Lea…

Replaced article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/1]:
- Optimal Control of Markov Decision Processes for Efficiency with Linear Temporal Logic Tasks
Yu Chen, Xuanyuan Yin, Shaoyuan Li, Xiang Yin

Reform led Nottinghamshire Council is blacklisting journalists for asking questions. An outrageous attack on press freedom. Sign the petition to get them to reverse this decision: https://

Reform: Stop hiding from press scrutiny!
Nigel Farage says he welcomes freedom of speech, even if people find it offensive. Surely that should apply to freedom of the press too? So why have Reform's Nottinghamshire councillors - who control the county council - been banned from talking to the Nottingham Post & Nottinghamshire Live, just because we wrote an article they didn't like? Our report asked legitimate questions about local government restructuring. That's our job. When councillors pick and choose which journalists get to...

Deploying Robust Decision Support Systems for Transit Headway Control: Rider Impacts, Human Factors and Recommendations for Scalability
Joseph Rodriguez, Haris N. Koutsopoulos, Jinhua Zhao
https://arxiv.org/abs/2509.08231

Deploying Robust Decision Support Systems for Transit Headway Control: Rider Impacts, Human Factors and Recommendations for Scalability
Service reliability is critical to transit service delivery. This paper describes headway control pilots conducted in two high-ridership Chicago bus routes between 2022 and 2023. A decision support system was developed for a bus holding strategy based on a reinforcement learning approach. For the pilots, a user interface enabled supervisors to monitor service and record applied actions. The first pilot tested terminal-based holding on a route affected by missed trips from absenteeism. The analy…

Former directors of the Centers of Disease Control and Prevention (CDC) on Monday
rebuked Health and Human Services Secretary Robert F. Kennedy Jr.
for his decision to fire its director, Susan Monarez.
https://thehill.com/policy/healthcare/

Reconfiguring Digital Accountability: AI-Powered Innovations and Transnational Governance in a Postnational Accounting Context
Claire Li, David Freeborn
https://arxiv.org/abs/2507.00288

Reconfiguring Digital Accountability: AI-Powered Innovations and Transnational Governance in a Postnational Accounting Context
This study explores how AI-powered digital innovations are reshaping organisational accountability in a transnational governance context. As AI systems increasingly mediate decision-making in domains such as auditing and financial reporting, traditional mechanisms of accountability, based on control, transparency, and auditability, are being destabilised. We integrate the Technology Acceptance Model (TAM), Actor-Network Theory (ANT), and institutional theory to examine how organisations adopt A…

MISC: Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints
Shivam Chaubey, Francesco Verdoja, Shankar Deka, Ville Kyrki
https://arxiv.org/abs/2507.02438

MISC: Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints
Shared control combines human intention with autonomous decision-making, from low-level safety overrides to high-level task guidance, enabling systems that adapt to users while ensuring safety and performance. This enhances task effectiveness and user experience across domains such as assistive robotics, teleoperation, and autonomous driving. However, existing shared control methods, based on e.g. Model Predictive Control, Control Barrier Functions, or learning-based control, struggle with feas…

A Stackelberg Game of Demand Response from the Aggregator's Perspective
Seangleng Khe, Parin Chaipunya, Athikom Bangviwat
https://arxiv.org/abs/2507.12708

A Stackelberg Game of Demand Response from the Aggregator's Perspective
In this paper, we investigate on the modeling of demand response activities between the single aggregator and multiple participating consumers. The model incorporates the bilevel structure that naturally occurs in the information structure and decision sequence, where the aggregator assumes the role of a leader and the participating consumers play the role of followers. The proposed model is demonstrated to be effective in load control, helping the aggregator to meet the target reduction while …

Talk with the Things: Integrating LLMs into IoT Networks
Alakesh Kalita
https://arxiv.org/abs/2507.17865 https://arxiv.org/pdf/2507.17865

Talk with the Things: Integrating LLMs into IoT Networks
The convergence of Large Language Models (LLMs) and Internet of Things (IoT) networks open new opportunities for building intelligent, responsive, and user-friendly systems. This work presents an edge-centric framework that integrates LLMs into IoT architectures to enable natural language-based control, context-aware decision-making, and enhanced automation. The proposed modular and lightweight Retrieval Augmented Generation (RAG)-based LLMs are deployed on edge computing devices connected to I…

Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems
Muhammad Sharshar, Ahmad Mohammad Saber, Davor Svetinovic, Amr M. Youssef, Deepa Kundur, Ehab F. El-Saadany
https://arxiv.org/abs/2507.22239

Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems
The increasing digitization of smart grids has improved operational efficiency but also introduced new cybersecurity vulnerabilities, such as False Data Injection Attacks (FDIAs) targeting Automatic Generation Control (AGC) systems. While machine learning (ML) and deep learning (DL) models have shown promise in detecting such attacks, their opaque decision-making limits operator trust and real-world applicability. This paper proposes a hybrid framework that integrates lightweight ML-based attac…

Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions
\'Angel Aso-Mollar, Diego Aineto, Enrico Scala, Eva Onaindia
https://arxiv.org/abs/2509.03953

Handling Infinite Domain Parameters in Planning Through Best-First Search with Delayed Partial Expansions
In automated planning, control parameters extend standard action representations through the introduction of continuous numeric decision variables. Existing state-of-the-art approaches have primarily handled control parameters as embedded constraints alongside other temporal and numeric restrictions, and thus have implicitly treated them as additional constraints rather than as decision points in the search space. In this paper, we propose an efficient alternative that explicitly handles contro…

Global Optimality in Multi-Flyby Asteroid Trajectory Optimization: Theory and Application Techniques
Zhong Zhang, Xiang Guo, Di Wu, Hexi Baoyin, Junfeng Li, Francesco Topputo
https://arxiv.org/abs/2508.02904

Global Optimality in Multi-Flyby Asteroid Trajectory Optimization: Theory and Application Techniques
Designing optimal trajectories for multi-flyby asteroid missions is scientifically critical but technically challenging due to nonlinear dynamics, intermediate constraints, and numerous local optima. This paper establishes a method that approaches global optimality for multi-flyby trajectory optimization under a given sequence. The original optimal control problem with interior-point equality constraints is transformed into a multi-stage decision formulation. This reformulation enables direct a…

Where are the Frontlines? A Visualization Approach for Map Control in Team-Based Games
Jonas Pech\'e, Aliaksei Tsishurou, Alexander Zap, Guenter Wallner
https://arxiv.org/abs/2507.19193

Where are the Frontlines? A Visualization Approach for Map Control in Team-Based Games
A central area of interest in many competitive online games is spatial behavior which due to its complexity can be difficult to visualize. Such behaviors of interest include not only overall movement patterns but also being able to understand which player or team is exerting control over an area to inform decision-making. Map control can, however, be challenging to quantify. In this paper, we propose a method for calculating frontlines and first efforts towards a visualization of them. The visu…

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
Dhruv S. Kushwaha, Zoleikha A. Biron
https://arxiv.org/abs/2508.09128 https://…

A Review On Safe Reinforcement Learning Using Lyapunov and Barrier Functions
Reinforcement learning (RL) has proven to be particularly effective in solving complex decision-making problems for a wide range of applications. From a control theory perspective, RL can be considered as an adaptive optimal control scheme. Lyapunov and barrier functions are the most commonly used certificates to guarantee system stability for a proposed/derived controller and constraint satisfaction guarantees, respectively, in control theoretic approaches. However, compared to theoretical gua…

Covariate balance under Bayesian decision theory
Andr\'e A. F. Fumis, Victor Fossaluza, Rafael B. Stern
https://arxiv.org/abs/2509.01828 https://arxiv.…

Covariate balance under Bayesian decision theory
We study optimal sample allocation between treatment and control groups under Bayesian linear models. We derive an analytic expression for the Bayes risk, which depends jointly on sample size and covariate mean balance across groups. Under a flat conditional prior, the covariate mean balance term simplifies to the Mahalanobis distance. Our results reveal that the optimal allocation does not always correspond to equal sample sizes, and we provide sufficient conditions under which equal allocatio…

Hierarchical Reinforcement Learning Framework for Adaptive Walking Control Using General Value Functions of Lower-Limb Sensor Signals
Sonny T. Jones, Grange M. Simpson, Patrick M. Pilarski, Ashley N. Dalrymple
https://arxiv.org/abs/2507.16983

Hierarchical Reinforcement Learning Framework for Adaptive Walking Control Using General Value Functions of Lower-Limb Sensor Signals
Rehabilitation technology is a natural setting to study the shared learning and decision-making of human and machine agents. In this work, we explore the use of Hierarchical Reinforcement Learning (HRL) to develop adaptive control strategies for lower-limb exoskeletons, aiming to enhance mobility and autonomy for individuals with motor impairments. Inspired by prominent models of biological sensorimotor processing, our investigated HRL approach breaks down the complex task of exoskeleton contro…

Decision Theory For Large Scale Outlier Detection Using Aleatoric Uncertainty: With a Note on Bayesian FDR
Ryan Warnick
https://arxiv.org/abs/2508.01988 https://

Decision Theory For Large Scale Outlier Detection Using Aleatoric Uncertainty: With a Note on Bayesian FDR
Aleatoric and Epistemic uncertainty have achieved recent attention in the literature as different sources from which uncertainty can emerge in stochastic modeling. Epistemic being intrinsic or model based notions of uncertainty, and aleatoric being the uncertainty inherent in the data. We propose a novel decision theoretic framework for outlier detection in the context of aleatoric uncertainty; in the context of Bayesian modeling. The model incorporates bayesian false discovery rate control for…

HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and Decision in Embodied Agents
Yibin Liu, Zhixuan Liang, Zanxin Chen, Tianxing Chen, Mengkang Hu, Wanxi Dong, Congsheng Xu, Zhaoming Han, Yusen Qin, Yao Mu
https://arxiv.org/abs/2508.02629

HyCodePolicy: Hybrid Language Controllers for Multimodal Monitoring and Decision in Embodied Agents
Recent advances in multimodal large language models (MLLMs) have enabled richer perceptual grounding for code policy generation in embodied agents. However, most existing systems lack effective mechanisms to adaptively monitor policy execution and repair codes during task completion. In this work, we introduce HyCodePolicy, a hybrid language-based control framework that systematically integrates code synthesis, geometric grounding, perceptual monitoring, and iterative repair into a closed-loop …

OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent
Bowen Chen, Zhao Wang, Shingo Takamatsu
https://arxiv.org/abs/2507.02353

OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent
Keyword decision in Sponsored Search Advertising is critical to the success of ad campaigns. While LLM-based methods offer automated keyword generation, they face three major limitations: reliance on large-scale query-keyword pair data, lack of online multi-objective performance monitoring and optimization, and weak quality control in keyword selection. These issues hinder the agentic use of LLMs in fully automating keyword decisions by monitoring and reasoning over key performance indicators s…

Agency Among Agents: Designing with Hypertextual Friction in the Algorithmic Web
Sophia Liu, Shm Garanganao Almeda
https://arxiv.org/abs/2507.23585 https://

Agency Among Agents: Designing with Hypertextual Friction in the Algorithmic Web
Today's algorithm-driven interfaces, from recommendation feeds to GenAI tools, often prioritize engagement and efficiency at the expense of user agency. As systems take on more decision-making, users have less control over what they see and how meaning or relationships between content are constructed. This paper introduces "Hypertextual Friction," a conceptual design stance that repositions classical hypertext principles--friction, traceability, and structure--as actionable values for reclaimin…

PRZK-Bind: A Physically Rooted Zero-Knowledge Authentication Protocol for Secure Digital Twin Binding in Smart Cities
Yagmur Yigit, Mehmet Ali Erturk, Kerem Gursu, Berk Canberk
https://arxiv.org/abs/2508.17913

PRZK-Bind: A Physically Rooted Zero-Knowledge Authentication Protocol for Secure Digital Twin Binding in Smart Cities
Digital twin (DT) technology is rapidly becoming essential for smart city ecosystems, enabling real-time synchronisation and autonomous decision-making across physical and digital domains. However, as DTs take active roles in control loops, securely binding them to their physical counterparts in dynamic and adversarial environments remains a significant challenge. Existing authentication solutions either rely on static trust models, require centralised authorities, or fail to provide live and v…

Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning and Fuzzy Logic in Four-Wheel Independent Steering and Driving Systems
Yizhi Wang, Degang Xu, Yongfang Xie, Shuzhong Tan, Xianan Zhou, Peng Chen
https://arxiv.org/abs/2508.16574

Hierarchical Decision-Making for Autonomous Navigation: Integrating Deep Reinforcement Learning and Fuzzy Logic in Four-Wheel Independent Steering and Driving Systems
This paper presents a hierarchical decision-making framework for autonomous navigation in four-wheel independent steering and driving (4WISD) systems. The proposed approach integrates deep reinforcement learning (DRL) for high-level navigation with fuzzy logic for low-level control to ensure both task performance and physical feasibility. The DRL agent generates global motion commands, while the fuzzy logic controller enforces kinematic constraints to prevent mechanical strain and wheel slippag…

Distributionally Robust Control with Constraints on Linear Unidimensional Projections
Alexandros E. Tzikas, Lukas Fiechtner, Arec Jamgochian, Mykel J. Kochenderfer
https://arxiv.org/abs/2508.07121

Distributionally Robust Control with Constraints on Linear Unidimensional Projections
Distributionally robust control is a well-studied framework for optimal decision making under uncertainty, with the objective of minimizing an expected cost function over control actions, assuming the most adverse probability distribution from an ambiguity set. We consider an interpretable and expressive class of ambiguity sets defined by constraints on the expected value of functions of one-dimensional linear projections of the uncertain parameters. Prior work has shown that, under conditions,…

Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems
Oren Fivel, Matan Rudman, Kobi Cohen
https://arxiv.org/abs/2507.00268

Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems
Deep reinforcement learning (DRL) has become a powerful tool for complex decision-making in machine learning and AI. However, traditional methods often assume perfect action execution, overlooking the uncertainties and deviations between an agent's selected actions and the actual system response. In real-world applications, such as robotics, mechatronics, and communication networks, execution mismatches arising from system dynamics, hardware constraints, and latency can significantly degrade pe…

A Quick Estimation of Fr\'echet Quantizers for a Dynamic Solution to Flood Risk Management Problems
Anna Timonina-Farkas
https://arxiv.org/abs/2508.19045 https://

A Quick Estimation of Fréchet Quantizers for a Dynamic Solution to Flood Risk Management Problems
Multi-stage stochastic optimization is a well-known quantitative tool for decision-making under uncertainty. It is broadly used in financial and investment planning, inventory control, and also natural disaster risk management. Theoretical solutions of multi-stage stochastic programs can be found explicitly only in very exceptional cases due to their variational form and interdependency of uncertainty in time. Nevertheless, numerical solutions are often inaccurate, as they rely on Monte-Carlo s…

Multi-Modal Model Predictive Path Integral Control for Collision Avoidance
Alberto Bertipaglia, Dariu M. Gavrila, Barys Shyrokau
https://arxiv.org/abs/2508.21364 https://…

Multi-Modal Model Predictive Path Integral Control for Collision Avoidance
This paper proposes a novel approach to motion planning and decision-making for automated vehicles, using a multi-modal Model Predictive Path Integral control algorithm. The method samples with Sobol sequences around the prior input and incorporates analytical solutions for collision avoidance. By leveraging multiple modes, the multi-modal control algorithm explores diverse trajectories, such as manoeuvring around obstacles or stopping safely before them, mitigating the risk of sub-optimal solu…

Double Check My Desired Return: Transformer with Target Alignment for Offline Reinforcement Learning
Yue Pei, Hongming Zhang, Chao Gao, Martin M\"uller, Mengxiao Zhu, Hao Sheng, Haogang Zhu, Liang Lin
https://arxiv.org/abs/2508.16420

Double Check My Desired Return: Transformer with Target Alignment for Offline Reinforcement Learning
Offline reinforcement learning (RL) has achieved significant advances in domains such as robotic control, autonomous driving, and medical decision-making. Most existing methods primarily focus on training policies that maximize cumulative returns from a given dataset. However, many real-world applications require precise control over policy performance levels, rather than simply pursuing the best possible return. Reinforcement learning via supervised learning (RvS) frames offline RL as a sequen…

Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control
J\'an Boldock\'y, Shahriar Dadras Javan, Martin Gulan, Martin M\"onnigmann, J\'an Drgo\v{n}a
https://arxiv.org/abs/2506.19646

Learning to Solve Parametric Mixed-Integer Optimal Control Problems via Differentiable Predictive Control
We propose a novel approach to solving input- and state-constrained parametric mixed-integer optimal control problems using Differentiable Predictive Control (DPC). Our approach follows the differentiable programming paradigm by learning an explicit neural policy that maps control parameters to integer- and continuous-valued decision variables. This policy is optimized via stochastic gradient descent by differentiating the quadratic model predictive control objective through the closed-loop fin…

Vision-driven River Following of UAV via Safe Reinforcement Learning using Semantic Dynamics Model
Zihan Wang, Nina Mahmoudian
https://arxiv.org/abs/2508.09971 https://

Vision-driven River Following of UAV via Safe Reinforcement Learning using Semantic Dynamics Model
Vision-driven autonomous river following by Unmanned Aerial Vehicles is critical for applications such as rescue, surveillance, and environmental monitoring, particularly in dense riverine environments where GPS signals are unreliable. We formalize river following as a coverage control problem in which the reward function is submodular, yielding diminishing returns as more unique river segments are visited, thereby framing the task as a Submodular Markov Decision Process. First, we introduce Ma…

Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection
Lin Lu, Yuyang Huo, Haojie Ren, Zhaojun Wang, Changliang Zou
https://arxiv.org/abs/2509.03297 …

Feedback-Enhanced Online Multiple Testing with Applications to Conformal Selection
We study online multiple testing with feedback, where decisions are made sequentially and the true state of the hypothesis is revealed after the decision has been made, either instantly or with a delay. We propose GAIF, a feedback-enhanced generalized alpha-investing framework that dynamically adjusts thresholds using revealed outcomes, ensuring finite-sample false discovery rate (FDR)/marginal FDR control. Extending GAIF to online conformal testing, we construct independent conformal $p$-value…

FICA: Faster Inner Convex Approximation of Chance Constrained Grid Dispatch with Decision-Coupled Uncertainty
Yihong Zhou, Hanbin Yang, Thomas Morstyn
https://arxiv.org/abs/2506.18806

FICA: Faster Inner Convex Approximation of Chance Constrained Grid Dispatch with Decision-Coupled Uncertainty
This paper proposes a Faster Inner Convex Approximation (FICA) method for solving power system dispatch problems with Wasserstein distributionally robust joint chance constraints (WJCC) and incorporating the modelling of the automatic generation control factors. The problem studied belongs to the computationally challenging class of WJCC with left-hand-side uncertainty (LHS-WJCC). By exploiting the special one-dimensional structure (even if only partially present) of the problem, the proposed F…

Safe Reinforcement Learning-based Automatic Generation Control
Amr S. Mohamed, Emily Nguyen, Deepa Kundur
https://arxiv.org/abs/2507.17868 https://arxiv.or…

Safe Reinforcement Learning-based Automatic Generation Control
Amidst the growing demand for implementing advanced control and decision-making algorithms|to enhance the reliability, resilience, and stability of power systems|arises a crucial concern regarding the safety of employing machine learning techniques. While these methods can be applied to derive more optimal control decisions, they often lack safety assurances. This paper proposes a framework based on control barrier functions to facilitate safe learning and deployment of reinforcement …

Computing optimal policies for managing inventories with noisy observations
Eugene Feinberg, Jefferson Huang, Pavlo Kasyanov, Thomas O'Neill
https://arxiv.org/abs/2507.19765

Computing optimal policies for managing inventories with noisy observations
This paper implements the Deep Deterministic Policy Gradient (DDPG) algorithm for computing optimal policies for partially observable single-product periodic review inventory control problems with setup costs and backorders. The decision maker does not know the exact inventory level, but can obtain noise-corrupted observations of them. The goal is to maximize the expected total discounted costs incurred over a finite planning horizon. We also investigate the Gaussian version of this problem wit…

Large Language Model-Driven Closed-Loop UAV Operation with Semantic Observations
Wenhao Wang, Yanyan Li, Long Jiao, Jiawei Yuan
https://arxiv.org/abs/2507.01930

Large Language Model-Driven Closed-Loop UAV Operation with Semantic Observations
Large Language Models (LLMs) have revolutionized robotic autonomy, including Unmanned Aerial Vehicles (UAVs). Recent studies have demonstrated the potential of LLMs for translating human instructions into executable control code for UAV operations. However, LLMs still face challenges from logical reasoning and complex decision-making, leading to concerns about the reliability of LLM-driven UAV operations. In this paper, we propose a LLM-driven closed-loop control framework that enables reliable…

Hierarchical Decentralized Stochastic Control for Cyber-Physical Systems
Kesav Kazam Ramachandran Anantharaman, Rahul Meshram
https://arxiv.org/abs/2506.22971

Hierarchical Decentralized Stochastic Control for Cyber-Physical Systems
This paper presents a two-timescale hierarchical decentralized architecture for control of Cyber-Physical Systems. The architecture consists of $N$ independent sub-processes, a global controller, and $N$ local controllers, each formulated as a Markov Decision Process (MDP). The global controller, operating at a slower timescale optimizes the infinite-horizon discounted cumulative reward under budget constraints. For the local controllers, operating at a faster timescale, we propose two differen…

AgenticControl: An Automated Control Design Framework Using Large Language Models
Mohammad Narimani, Seyyed Ali Emami
https://arxiv.org/abs/2506.19160 http…

AgenticControl: An Automated Control Design Framework Using Large Language Models
Traditional control system design, reliant on expert knowledge and precise models, struggles with complex, nonlinear, or uncertain dynamics. This paper introduces AgenticControl, a novel multi-agent framework that automates controller design using coordinated Large Language Model (LLM) agents. Through structured JSON communication, these agents handle tasks including controller selection, scenario design, parameter optimization, performance evaluation, and decision-making. Through an actor-crit…

A Digital Twin Framework for Generation-IV Reactors with Reinforcement Learning-Enabled Health-Aware Supervisory Control
Jasmin Y. Lim, Dimitrios Pylorof, Humberto E. Garcia, Karthik Duraisamy
https://arxiv.org/abs/2506.17258

A Digital Twin Framework for Generation-IV Reactors with Reinforcement Learning-Enabled Health-Aware Supervisory Control
Generation IV (Gen-IV) nuclear power plants are envisioned to replace the current reactor fleet, bringing improvements in performance, safety, reliability, and sustainability. However, large cost investments currently inhibit the deployment of these advanced reactor concepts. Digital twins bridge real-world systems with digital tools to reduce costs, enhance decision-making, and boost operational efficiency. In this work, a digital twin framework is designed to operate the Gen-IV Fluoride-salt-…